Source of the Dataset :

The dataset was acquired from Md Kazi Sajiduddin on kaggle. It was created around July 2023. Jikan Application Programming Interface (4.0.0) was used to extract the anime dataset via the My Anime list. The original dataset retrived anime-related data, including the original title, the english title, Demographics, Start season, Airing date,Format, Studios, Synopsis, Production house, The User ID and the scores given by the users. MyAnimeList.

What is My Anime List?

Frequently shortened as MAL, MyAnimeList is a volunteer-run website that provides social networking and social cataloging services for fans of anime and manga. Users of the website can score and arrange anime and manga using a system similar to a list. It offers a comprehensive database on anime and manga and makes it easier to find users with similar interests.

What will my project include?

The data included 24,985 anime titles that were rated by users on My Anime List. The original dataset had a plethora of information including The original title, english title, Demographics, Start season, Airing date,Format, Studios, Synopsis, Production house, The User ID and the scores given by the users. For my project, I will examine the top 1000 anime titles in the dataset to identify recurring themes. Additionally, I will also visualise if the Demographics of the anime, taking a look at the intended auidence for the title as it may help us understand the relevance of themes better. Thus, I make sure only these columns are retrieved from the rawdata. Also, it is to be noted that the scores are not included in the project as the list already consists of highly rated titles with very little deviation, so including the same would be redundant. The project will instead calculate the total count and same will be included for reference.

Folders in my project:

The /Data consists of the raw data acquired from kaggle, /figures consist of the Plots generated in the project and /images consist of the image used in the project.

Importing the data

# Selecting specific columns
cols <- c('themes', 'demographics')
# Specifying the file path of the dataset
file_path <- here::here("Data/anime.csv")
#n_max is set to 1000 in order to retrieve the top 1000 titles
data <- read_csv(file_path, col_select = cols,n_max = 1000) 

# Renaming the columns
data <- rename(data,
            Themes= themes,
            Demographics= demographics
            )

A table of the total theme count from top rated 1000 anime titles on My Anime List:

kable(theme_counts, format = "markdown")
Themes Count
School 251
Adult Cast 98
Historical 80
Psychological 79
Super Power 73
Mythology 63
Military 62
Isekai 60
Gore 48
Mecha 48
Gag Humor 44
Iyashikei 39
Parody 39
Music 36
Love Polygon 35
Team Sports 32
Reincarnation 27
Time Travel 26
Workplace 26
CGDCT 25
Harem 25
Organized Crime 25
Space 25
Otaku Culture 24
Survival 23
Detective 22
Vampire 22
Romantic Subtext 20
Childcare 19
Martial Arts 19
Samurai 19
Video Game 17
Mahou Shoujo 16
Strategy Game 13
Anthropomorphic 12
Performing Arts 11
Visual Arts 11
Racing 10
Combat Sports 9
Delinquents 7
High Stakes Game 7
Idols (Female) 6
Showbiz 6
Reverse Harem 4
Crossdressing 2
Educational 1
Magical Sex Shift 1
Medical 1
Pets 1

An interactive plot of the Theme count

# Assigning the rainbow theme to each unique theme in theme_count
theme_colors <- rainbow(length(unique(theme_counts$Themes)))
#setting the hover text
hover_text <-paste('Theme:', theme_counts$Themes, '<br>Count:' , theme_counts$Count)  

# Creating the first graph as fig_1 with ggplot

fig1 <-
    ggplot(theme_counts, aes(x = reorder(Themes,-Count), y = Count, text = hover_text)) +
    geom_bar(stat = 'identity', fill = theme_colors) +
  
# To make the intervals on Y axis 50 and remove the gap between Y axis and 0    
    scale_y_continuous(breaks = seq(0, 250, by = 50),expand =c(0,0)) + 
# setting title
    ggtitle("Themes of the top rated anime of 2023") + 
# defining labels
    labs(x = "Themes (of top 1000 anime titles)", y = "Count (of themes)") + 

# customising theme 
    theme_minimal() +
    theme(
      
        plot.background = element_rect(fill = 'black'),  # To create a black background
        panel.background = element_rect(fill = 'black'), # To create a black panel background
        panel.grid.major = element_line(color = 'transparent'),  # To make major gridlines transparent
        axis.line = element_line(color = '#FFFFFF'),  # axis lines colour set as White
        axis.text = element_text(color = '#EEB4B4'),  # axis text colour set as rosybrown2
        axis.title = element_text(color = 'skyblue'), # axis title colour set as skyblue
        plot.title = element_text(color = 'skyblue', size = 18, hjust = 0.5, face = 'italic'),
        axis.text.x = element_text(angle = 45, hjust = 1, size = 7)  # x-axis text angle was adjusted to make it more readable
        ) +
  
 # Removing the legend as the name of the column and count can be seen in the hover text
        guides(fill = FALSE)  

#assigning the plot to plotly fr an interactive graph
fig1 <- ggplotly(fig1, tooltip= 'text')

# Saving the figure in the figures folder
ggsave(here::here('Figures', 'Themes_graph.png'))

Printing the graph

# A conditional statement is added here so an interactive graph is displayed when the document is a html page, or else as a png

# If html page, display the plotly graph
if (knitr::is_html_output()) {
  fig1
} else {
# Print the PNG image (for pdf)
knitr::include_graphics('Figures/Themes_graph.png')
}

Bonus Graph: Demographics

We can better understand which themes and tropes appeal to particular audiences by using demographic data.Therefore we will take a look at demographics as well. Typical demographics consist of:

  • Shounen: Targeted towards young boys
  • Shoujo: Targeted towards young girls
  • Seinen: Targeted towards adult men
  • Josei: Targeted towards adult women
  • Kids: Targeted towards Younger auidence
kable(dem_counts, format = "markdown")
Demographics Count
Shounen 317
Seinen 128
Shoujo 53
Josei 15
Kids 6

A Graph that plots the demographics of the top 1000 anime of 2023

# Creating the second bar graph in ggplot
# Setting up rainbow themes for the graph by assigning a colour to each unique value
dem_colors <- rainbow(length(unique(dem_counts$Demographics))) 

#setting the hover text
hover_text <-paste('<br>Count:' , dem_counts$Count) 

#Creating the plot with ggplot
fig2 <- 
    ggplot(dem_counts, aes(x =reorder(Demographics,Count), y = Count, fill = Demographics,text= hover_text)) +
    geom_bar(stat= 'identity')+
  
  # defining labels
    labs(x= "Demographics (of top 1000 anime titles)", y= "Count", title= "Bar graph of demographics") + 
  
  # To create a horizontal chart 
    coord_flip() + 
    theme_minimal() +
  
  # Customising theme
  
  theme(
        plot.background = element_rect(fill = 'black'),  # To create black background
        panel.background = element_rect(fill = 'black'), # To create a black panel background
        panel.grid.major = element_line(color = 'transparent'),  # To make major gridlines transparent
        axis.line = element_line(color = '#FFFFFF'),  # axis lines colour set as White
        axis.text = element_text(color = '#EEB4B4'),  # axis text colour set as rosybrown2
        axis.title = element_text(color = 'skyblue'), # axis title colour set as skyblue
        plot.title = element_text(color = 'skyblue', size = 14),  # Plot title colour set to blue & size was adjusted
        
        ) +
  
    scale_fill_manual(values = dem_colors) +  # setting the colours in the plot
  
    # removing the legend because the plot is interactive and the names and count can be seen when clicked on
    guides(fill = FALSE) 

#assigning the plot to plotly for an interactive graph
fig2 <- ggplotly(fig2, tooltip = 'text')

# Saving the figure in the figures folder
ggsave(here::here('Figures', 'Demographics.png'))

Printing the graph :

#A conditional statement is added here so an interactive graph is displayed when the document is a html page, or else as a png in pdf
if (knitr::is_html_output()) {
  fig2
} else {
  # Print the PNG image (for pdf)
  knitr::include_graphics("Figures/Demographics.png")
}

Insights:

2023 saw a lot of successful anime releases in a variety of genres. However, a clear trend became apparent: audiences were drawn to stories set in schools. Other themes that did well were Adult Cast, Historical, Psychological, Super Power, Mythology, Military, and Isekai. One prominent genre of anime was shounen, which catered to young boys. The predominance of themes like school,action and adventure, which are typically popular with this demographic, may be explained by this focus on a male audience.

Seinen, an anime series targeted at adult men, is among the top demographics, though, indicating a more complex picture. Seinen anime often explores mature themes like psychology and complex character development (adult cast), which could explain why these themes were also highly rated in 2023.

Closing remarks:

With this module, I was able to learn a new skill at my own pace. I can say that over time, my proficiency with R Studio and Github has improved somewhat. I also took advantage of this opportunity to research different themes and packages that could help me with my project. Exploring plotly was also one of the aspects of the project that I enjoyed, as creating interactive plots with informative tooltip assists in delivering information in a compact manner. I also looked into using renv to manage project environments and make sure the necessary packages are installed correctly across various devices.

If I had more time to work on the project, I would have loved to plot all of the variables based on various criteria (for example, contrasting highly rated versus low rated anime titles) to have a comprehensive understanding of criteria that make an anime series highly rated. One of the limiations of my project can be that the plots were based on the top 1000 titles, for a more comprehensive analysis, data of all the titles can be visualised by future projects.

References :